xy2unit {spectralGP} | R Documentation |
Scales locations to (0,1)^d so that they can be related to the
gridpoints in a spectral GP representation. The
locations.scale
argument allows one to scale the
locations
to a separate set of locations. E.g., if one wants
to predict over a certain set of locations, but has a separate
training set of locations that lie within the prediction set, one
would use the prediction locations as the locations.scale
argument.
xy2unit(locations, locations.scale = NULL)
locations |
A two-column matrix-like object (vector for one-dimensional data) of locations to be scaled. |
locations.scale |
A two-column matrix-like object (vector for one-dimensional data) of locations that provides the function with the min and max coordinates in each direction. |
One may want to use both training and prediction locations as the
locations.scale
argument to ensure that all locations of
interest will lie in (0,1)^d and be able to be related to the gridpoints.
A matrix (vector for one-dimensional data) of scaled locations lying in (0,1)^d.
Christopher Paciorek paciorek@alumni.cmu.edu
Type 'citation("spectralGP")' for references.
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. library(spectralGP) gp1=gp(c(128,128),matern.specdens,c(1,4)) n=100 locs=cbind(runif(n,0.2,1.2),runif(n,-0.2,1.4)) locs.predict=cbind(runif(n,-0.4,0.8),runif(n,-0.1,1.7)) scaled.locs=xy2unit(locs,rbind(locs,locs.predict)) scaled.locs.predict=xy2unit(locs.predict,rbind(locs,locs.predict)) train.map=new.mapping(gp1,scaled.locs) predict.map=new.mapping(gp1,scaled.locs.predict) plot(locs,xlim=c(min(locs[,1],locs.predict[,1]),max(locs[,1],locs.predict[,1])),ylim=c(min(locs[,2],locs.predict[,2]),max(locs[,2],locs.predict[,2]))) points(locs.predict,col=2) plot(scaled.locs,xlim=c(0,1),ylim=c(0,1)) points(scaled.locs.predict,col=2)